{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import analytics.basket_index as bkt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from quantlib.time.api import Schedule, Rule, Date, Period, WeekendsOnly\n",
    "from quantlib.settings import Settings\n",
    "from db import dbengine\n",
    "from ipywidgets import widgets\n",
    "engine  = dbengine('serenitasdb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = widgets.Dropdown(\n",
    "    options=['IG', 'HY', 'EU'],\n",
    "    value='IG',\n",
    "    description='Index:',\n",
    "    disabled=False,\n",
    ")\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_1 = widgets.IntSlider(value=30, min=22, max=30, description = 'Series')\n",
    "w_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_date = (pd.datetime.today() - pd.offsets.BDay(1)).normalize().date()\n",
    "index = w.value\n",
    "series = w_1.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_string = \"select * from index_members(%s, %s)\"\n",
    "df = pd.read_sql_query(sql_string, engine, params=(index + str(series), trade_date), index_col=['markit_ticker'])\n",
    "df1 = pd.read_sql_query(sql_string, engine, params=(index + str(series-1), trade_date), index_col=['markit_ticker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removals\n",
    "df1.loc[df1.index.difference(df.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Additions\n",
    "df.loc[df.index.difference(df1.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dp_top = pd.DataFrame()\n",
    "date_range = pd.bdate_range(trade_date - 52 * pd.offsets.Week(), trade_date, freq='5B')\n",
    "for d in date_range:\n",
    "    temp = bkt.MarkitBasketIndex(index, series, ['5yr',], value_date=d.date())\n",
    "    temp1 = temp.survival_matrix(np.array([np.datetime64(temp.maturities[0]).view('int') + 134774]))\n",
    "    temp1 = pd.DataFrame(1 - temp1[0], index=temp1[1], columns=[d.date()])\n",
    "    dp_top = pd.concat([dp_top, temp1], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Top 20 highest cumulative\n",
    "dp_top = dp_top.sort_values(trade_date, ascending=False)[0:20]\n",
    "dp_top = dp_top.join(df1.name).set_index('name', drop=True)\n",
    "dp_top.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'market implied default probabilities to ' + str(temp.maturities[0])\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(dp_top.T)\n",
    "ax.set(xlabel='date', ylabel='probability',\n",
    "       title=title)\n",
    "ax.legend(dp_top.index, loc='upper center', bbox_to_anchor=(1.3, 1), ncol=1)\n",
    "fig.set_size_inches(10, 6)\n",
    "fig.tight_layout()"
   ]
  }
 ],
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